{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib常见使用方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://matplotlib.org/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] #中文支持\n",
    "plt.rcParams['axes.unicode_minus'] = False #正常显示负号\n",
    "\n",
    "plt.rcParams['font.size'] = '16' # 设置字体大小\n",
    "\n",
    "plt.rcParams['lines.linewidth'] = 2 #设置线条宽度\n",
    "plt.rcParams['lines.color'] = 'red'#设置线条颜色\n",
    "plt.rcParams['lines.linestyle'] = '--' #线条样式\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 直方图 \n",
    "- 查看数据分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0., 2., 1., 2., 5., 1., 2.]),\n",
       " array([150, 155, 160, 165, 170, 175, 180, 185]),\n",
       " <a list of 7 Patch objects>)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "height = [168,155,182,170,173,161,155,173,176,181,166,172,170]\n",
    "bins = range(150,190,5)\n",
    "\n",
    "plt.hist(height,bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 条形图 \n",
    "- 各个属性之间的比较关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '中文标题')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "classes = ['class1','class2','class3']\n",
    "scores = [70,80,60]\n",
    "plt.bar(classes,scores)\n",
    "plt.title('中文标题')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 转换方向"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(classes,scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEVCAYAAAABwEUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAYA0lEQVR4nO3dfZRV9X3v8fdXFENAEXSKisWJ0S5jE2l0lkAKZmIFn6JNbWOeTNJIgkGvSe1NjLY2N1pjDDcrvZqoudyaVq3Gh9b4GJaYXibBCFqIEh8aY4KwFK4EK4IkGJD53j/2FhAOzIDsc4bZ79daLM/ZD+f3nTX4OZvfb+/fLzITSVL/tlurC5AkVc+wl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsVQsR8U8RcX4vjmuLiDG9OO76iDhhB2vZLSIGbrZtzx35LKm3dm91AVKTvFb+6UkbcGtEvDMzV2/juFeBtQARMR/Yc5PPHwT8PDP/tNz/K+D/lfveBZwFXBQRg4DhwLPAQKBju34iaTsY9qqLdTQI+4gYAAzIzLUAmflURNwEHA38aJPj9gDWA0OAfYG3Am0RMSozj+6h7d8BdwDdwP7AnMzsiIiPAH+QmZe86Z9O6oFhr34pIv4RmAgsLzeNAk6JiMnl+wOB64Anga9FRAAHbHL8JyPirRRX7Csp/l/5IMVV++eBCUA78F7g3B7K2Q2YT/Fl8UpRXvwS+ElZ64+AmZn51TfxI0vbZNirv1oLfC0zvwMQEd8Gntjk/VeA7sy8BbglIvYGHs/Mg1//gIi4mOKq/w1X3hHRBawC/iewNiKeAn5L8cWQZduDgc9n5kyKbprXgzzK/64C/lv55wHg93bqTy9txrBXf9W9Pcdk5qqI+EVEjM7MBeXmk4H/3uC89wN7AP8AfDEzj4ANXw4vvv6FUm4bDfyQ4qoeiqv8I8q2dwP+DLiK4ktCqoxhr/5qN4pB0E+X70cBp27y/kDg2s3OuQb4AvDxiDgGGJyZcxp89heAR4AbgakR8XpwAxARlwPXZObzFN02T1OMGXwL+BzwIjAA+H1gcXmad+OoUt56qf5qD4punI7M7ABu2+z99PKYDTLz+8DBEfFB4NvABZt/aHn75nPAo8ATwCnAORRh/rru8nwycyFFF85MYCTwd2W7jwF/CTwM/By4f6f81NJWGPbqr84G/vc29l8KfKXB9k8DNwMvZ2ajAP4hMHWT94dRXKHfu8m2y4AxEXFSRAwF3ga8G/hP4FiKwdqHKL4ofkjxL4qRPf9I0o4z7NUvZWZ3vnGxht3Y5O97uf8N/foRcSJwE8XV96sRcVdEjN3scx/PzBc3+bwjKb44DqDoKnotM18FPgEsoLiFc3Z5zL7AGGA/4CTg5Mx8jGIM4NXyi0GqhH32qovBFA8uvUFEHAT8NcVg7NPARzLzl+W+PwOuioj9gS7g/Mz8r/LUPYGBmfm98tjrgYMo77rJzAfK7ecA/5yZt0fEROAMioenBgJ3RMQwYCjwFuA4oNEYgfSmhStVqc7K++vPBu7OzKVbOeZw4PDMvHMHPv8ND21JrWLYS1IN2GcvSTVg2EtSDfTZAdr99tsv29vbW12GJO0y5s+f/2JmtjXa12fDvr29nXnz5rW6DEnaZUTE4q3tsxtH2k4rVqzg5JNPpqOjg7PPPhuAyZMnM27cOC677LIWVyc1ZthL2+nGG2/kYx/7GPPmzeOVV15h2rRprF+/njlz5rBw4UKeeeaZVpcobcGwl7bTvvvuyxNPPMHLL7/Mc889x7PPPssZZ5wBwKRJk3jwwQdbXKG0JcNe2k7jx49n8eLFXHXVVbzjHe9g7dq1jBxZTG0zfPhwli1b1uIKpS0Z9tJ2uuSSS/jOd77Dl7/8ZQ4//HBuvvlm1qxZA8Dq1avp7u7NVPpScxn20nZasWIFjz/+OOvXr+fhhx/mwgsv3NB1s2DBArxlWH1RJbdelpM73USx1Nr8zDw7Iq6jWKHnvsz0lgXtsi666CI+9alPsXjxYsaNG8f555/PhAkTWLp0KTNmzGDu3LmtLlHaQlVX9h8HbioXidgrIi6gmAxqHHBIRBxWUbtS5Y455hiefPJJVq9ezQMPPMDee+9NV1cXY8eOZdasWQwd6kzF6nuqeqjqv4B3RsQ+FAs7rKRYKQiKFXvGA96fpn5j2LBhG+7IkfqiqsL+QYpVeD5HsTrPQGBJue8l4KhGJ0XEFGAKwKhRoyoqTX1R+4X3tbqEfmvRFae0ugT1AVV14/wP4LOZeSnF+pofBQaV+4Zsrd3MnP76GqFtbQ2nd5Ak7YCqwn4Y8K5y4YYxwBUUXTcAo4FFFbUrSWqgqm6crwH/BBxMsczaPwCzI+JAirU3x27jXEnSTlZJ2GfmI8AfbrotIjqBicC0zFxZRbuSpMaaNsVxZq5g4x05kqQm8glaSaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqwLCXpBpo2tw42rprr72WW2+9FYCXX36ZMWPG8Nprr/HUU09xyimncPHFF7e4Qkm7Oq/s+4CpU6fS1dVFV1cXEyZM4O1vfzvr169nzpw5LFy4kGeecQVHSW+OYd+HLFmyhGXLlvH8889vWM900qRJPPjggy2uTNKuzrDvQ66++mqmTp3Kb37zG0aOHAnA8OHDWbZsWYsrk7SrM+z7iO7ubmbNmkVnZydDhgxhzZo1AKxevZru7u4WVydpV2fY9xGzZ89mzJgxRARHH330hq6bBQsW0N7e3triJO3yKrkbJyKmAh8q3+4DPFy2dQRwX2ZeVkW7u7L777+fY489FoAPfOADTJgwgaVLlzJjxgzmzp3b4uok7eoqubLPzGszszMzO4HZwK+AAZk5DjgkIg6rot1d2eWXX87pp58OwN57701XVxdjx45l1qxZDB06tMXVSdrVVXqffUSMBEYAycb1Z2cC4wHvJ9yGYcOGbbgjR5LerKofqjoXuBY4E1hSbnsJOKrRwRExBZgCMGrUqB1utP3C+3b4XG3boitOaXUJknZAZQO0EbEb8D6gC1gNDCp3Ddlau5k5PTM7MrOjra2tqtIkqXaqvBtnAvBwZiYwn6LrBmA0sKjCdiVJm6myG+cE4Mfl6zuB2RFxIHASMLbCdiVJm6ks7DPzbzZ5vSoiOoGJwLTMXFlVu5KkLTVt1svMXMHGO3IkSU3kE7SSVAOGvSTVgGEvSTVg2EtSDRj2klQDhr0k1YBhL0k1YNhLUg0Y9pJUA4a9JNWAYS9JNWDYS6qNc845h3vuuQeAyZMnM27cOC67rB5LYhv2kmph9uzZvPDCC5x66qnccccdrF+/njlz5rBw4UKeeab/r5Jq2Evq99atW8dnPvMZ2tvbueuuu+jq6tqwxvOkSZN48MEHW1xh9Qx7Sf3eDTfcwBFHHMEFF1zAI488wtVXX83IkSMBGD58OMuWLWtxhdVr2nz2ktQqjz76KFOmTGH//ffnzDPP5KGHHmLNmjUArF69mu7u7hZXWD2v7CX1e4ceeigLFy4EYN68eSxatGhD182CBQtob29vYXXNUemVfURcA8zIzHsi4jrgCOC+zKzH8LekPmHy5MmcddZZ3HLLLaxbt46uri5OO+00li5dyowZM5g7d26rS6xcZWEfEROA/cugPx0YkJnjIuK7EXFYZvb/4W9JfcJee+3F7bff/oZtXV1dPPDAA1xwwQUMHTq0RZU1TyVhHxF7AP8H+EFE/CnQycb1Z2cC44Etwj4ipgBTAEaNGlVFaZJ2gvYL72t1CTvJYPjp/FYX8QaLrjilks+tqs/+E8BTwDTgGOBcYEm57yVgRKOTMnN6ZnZkZkdbW1tFpUlS/VTVjfNuYHpmvhAR/wK8BxhU7huCA8OS1FRVhe4vgUPK1x1AO0XXDcBoYFFF7UqSGqjqyv464LsR8WFgD4o++7sj4kDgJGBsRe1KkhqoJOwz8xXgg5tui4hOYCIwLTNXVtGuJKmxpj1Bm5kr2HhHjiSpiRwolaQaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqwLCXpBow7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGqhkpaqI2B1YWP4BOA/4C+Bk4JHMPLeKdiVJjVV1ZX8k8L3M7MzMTmAgMB44Bvh1RBxfUbuSpAaqCvuxwPsj4pGIuA74E+DfMjOB+4EJjU6KiCkRMS8i5i1fvryi0iSpfqoK+/8Ajs/MY4A9gEHAknLfS8CIRidl5vTM7MjMjra2topKk6T6qaTPHvhZZv6ufD2PjYEPMAQHhiWpqaoK3RsjYnREDAA+AAym6LMHGA0sqqhdSVIDVV3ZXwrcDARwN3AZMDsirgROLP9IkpqkkrDPzCco7sjZoLwD5xTgysx8top2JUmNVXVlv4XMXAP8a7PakyRt5ECpJNWAYS9JNWDYS1INGPaSVAOGvSTVgGEvSTWwzbCPiCERMap8fVqD/e+tqjBJ0s7T0332BwDHR8TbgGURcSlwMPAacBNwDvCjakuUJL1ZPYX9emAtcDjwAsX8NmcCCVwJnFVpdZKknaKnsL8D2BPYD3il3HYycCpwe2auqrA2SdJO0tMA7Z8D3wDmAj8tt90AfBo4NiJOr7A2SdJO0tOV/W4UV/ZPl8f+FPh+uf3PgXsprv4lSX1YT2H/HMUUxVOBRynCfizwMnAo8O+VVidJ2il66sY5ozzmceAqirVjP0wxQHtCZn612vIkSTtDT2G/CvgqMBH4F4q7cf4QeJaNK09Jkvq4bXbjZOadwJ0RMQFYm5kPR0QH8Gvg4YiYlJkzm1GoJGnH9fQE7d4R8TngEuCAiPgkMB/oBhYDN0XEodWXKUl6M3rqxrkK+BnwO2ABcBjFoOxxwBjg/sz85dZOjogREfFo+fq6iJgTERfvlMolSb3WU9hfCawE9gHeCbwIrKPov98/M8/s4fxvAIPK+/EHZOY44JCIOOzNlS1J2h49hf0ngbOB4cBBwEJgSmbuD6yKiA9v7cSIOA74DcU0C53AbeWumWxlcDcipkTEvIiYt3z58u35OSRJ27DNsM/Mv8rMzwK/ysxrKaZJODsivkDxQNUXI2LE5udFxEDg74ALy02DgSXl65eALc4p25uemR2Z2dHW1rZDP5AkaUs9PVS1uaMorvSHUXTRnJ2ZyxocdyFwTWa+HBEAq4FB5b4hOI++JDVVb8N+cET8AcXDVC8ATwEXUDxs1cjxwHERcS7wR8Aoiqdx5wKjKaZfkCQ1SW/DfiHwJeAx4OsU4b0XcENEXJmZ6zc9ODOPff11RHQBpwGzI+JA4CSKKRckSU3Sq7DPzE9tvi0i9gY+24tzO8vjOymexJ2WmSu3q0pJ0puyvX32G5Rz2U/bjuNXsPGOHElSEzlQKkk1YNhLUg0Y9pJUA4a9JNWAYS9JNWDYS1INGPaSVAOGvSTVgGEvSTVg2EtSDRj2klQDhr0k1YBhL0k1YNhLUg0Y9pJUA4a9JNVAZWEfEcMjYmJE7FdVG5Kk3qkk7CNiGHAvcAwwKyLaIuK6iJgTERdX0aYkaet2eFnCHhwJ/HVmzi2D/zhgQGaOi4jvRsRhmflMRW1LkjZTSdhn5o8AIuJYiqv74Wxcf3YmMB7YIuwjYgowBWDUqFFVlCZJtVRln30AHwJWAAksKXe9BIxodE5mTs/MjszsaGtrq6o0SaqdysI+C+cCPwPeAwwqdw2psl1J0paqGqD9UkR8ony7D3AFRdcNwGhgURXtSpIaq2qAdjpwW0R8GngCuBP4cUQcCJwEjK2oXUlSA1UN0K4AJm66LSI6y23TMnNlFe1Kkhqr6sp+C+UXwG09HihJ2ukcKJWkGjDsJakGDHtJqgHDXpJqwLCXpBow7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqwLCXpBqoasHxoRExIyJmRsT3I2JgRFwXEXMi4uIq2pQkbV1VV/YfA76ZmZOAF4APAwMycxxwSEQcVlG7kqQGKgn7zLwmMx8o37YBZ7Jx/dmZwPhG50XElIiYFxHzli9fXkVpklRLlfbZR8Q4YBjwHLCk3PwSMKLR8Zk5PTM7MrOjra2tytIkqVYqC/uIGA58CzgLWA0MKncNqbJdSdKWqhqgHQjcDlyUmYuB+WzsuhkNLKqiXUlSY1VdYU8GjgL+NiK6gAA+HhHfBM4A7quoXUlSA7tX8aGZeS1w7abbIuJuYCIwLTNXVtGuJKmxSsK+kcxcwcY7ciRJTeRAqSTVgGEvSTVg2EtSDRj2klQDhr0k1YBhL0k1YNhLUg0Y9pJUA4a9JNWAYS9JNWDYS1INGPaSVAOGvSTVgGEvSTVg2EtSDRj2klQDlYZ9RIyIiNnl6z0i4p6I+ElEnFVlu5KkN6os7CNiGHA9MLjcdB4wPzP/GPiLiNirqrYlSW9U5ZX9euBDwKryfScblyX8MdBRYduSpE1UFvaZuWqzhcUHA0vK1y8BIzY/JyKmRMS8iJi3fPnyqkqTpNpp5gDtamBQ+XpIo7Yzc3pmdmRmR1tbWxNLk6T+rZlhPx8YX74eDSxqYtuSVGu7N7Gt64EfRMQE4Ajg4Sa2LUm1VvmVfWZ2lv9dDEwEfgIcn5nrq25bklRo5pU9mbmUjXfkSJKaxCdoJakGDHtJqgHDXpJqwLCXpBow7CWpBgx7SaoBw16SasCwl6QaMOwlqQYMe0mqAcNekmrAsJekGjDsJakGDHtJqgHDXpJqwLCXpBow7CWpBpoa9hFxXUTMiYiLm9muJNVd08I+Ik4HBmTmOOCQiDisWW1LUt0188q+k43rz84ExjexbUmqtWYuOD4YWFK+fgk4avMDImIKMKV8uzoinm5Sba20H/Biq4vorfh6qyvoE/yd7Xp2md/Zm/x9Hby1Hc0M+9XAoPL1EBr8qyIzpwPTm1hTy0XEvMzsaHUd6j1/Z7sef2fN7caZz8aum9HAoia2LUm11swr+zuB2RFxIHASMLaJbUtSrTXtyj4zV1EM0s4F3peZK5vVdh9Xq26rfsLf2a6n9r+zyMxW1yBJqphP0EpSDRj2fUREDG51Ddq2iPjniHg0Iroi4taIGNDqmtSziPj7iHgoIr4fEUNaXU+rGPYtEBHDIuLIiHhXRBxUbp4REe+JiM6IeF9EbPV+WbXUeZnZCawAJrW4FvUgIt4DTAD+mOJhzinbPqP/MuybJCKGR8QrEfEWoA34JPB14PVpI14BhgJfAt4BvLUlhaq39gN+0+oi1KMTgB9kMTh5P/BMi+tpGcO+eSYCbwGOzcxfAN8DHgGejoiLgFczcwbwK4q/nP/ZulK1Dd+KiJ8DBwJzWl2MejSC4ol9MnNhZt7T4npaxrBvnhOBq4ETy/75t1Nc4b8N2At4tZwcbhhwWETs1bJKtS3nAUcA/wFc2OJa1LNVFE/sExHHRMQXW1xPyxj2zTMOuAz4E2Bfiv7edwJjgN8Ce1D0LR5S7tuvNWWqJ5nZTdFn7xdy3/cTin9VA7wXWNPCWlqqmU/Q1lZEHEkR3v8KtAMJXAu8PzO/GRF7ANdn5ncj4ijg6sxc1Kp6tU3fiojflq8/2tJK1Bt3A8dHxEMUE6F9pMX1tIxh3xwnAJeXwf735fvHACKiHRgF/Kxl1alXMvMvW12Dtk85MHteq+voCwz75jgB+EL5+v8CfwscBJxFMXjUCfzVJvdtR0TskZnrml2opP7JPvsmyMzjM/Ox8vUsink61lIM9L0G/ILiVsu7KL4E/hdwbmuqldQfOTdOi5V35qzLzLWtrkVS/2XYS1IN2I0jSTVg2EvbsLUJ6sq7qKRdht04UikihgG/T/EcxIrMfD4ifkzxpOxAIICFFHPi3EbxsM4sikH2tRR3t12SmbNbUL60TV7ZSxv1doK684C/Ac6guK3215l5IvBrXFtZfZRhL5V6M0EdRdiPoZjDaHJ56ut3Uh2Ymc81t2qpdwx7qdSbCeoown4BxdX/58v3AyNiBPCuiLg3Ivz/Sn2OfymljXozQd3zFP30t2TmkxRzHZ0CdAO/R/HMRHfTK5d64ACttImI6KCYoO4rm0xQ99GI+DbwDWBPYD7wQ+CnwGKKVZBeA+4E3p2ZX29N9dLWOTeO1MA2Jqh7FvgjYDhwADCVYvbLacAVFAO2Up9j2EuliNgHeD/bmKCOonvnUuDfgYOBLuBVYDBFV87hwPJm1i31hn320kaT6HmCuqMz830Uq44to+jSuZdifYLjgS9FxAebX7q0bfbZSw30doK6iNgd6N50UDYiBmTm+qprlLaHYS9JNWA3jiTVgGEvSTVg2EtSDRj2klQDhr0k1cD/B43mVuVDzR+0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name = ['A班','B','C']\n",
    "x = [1,2,3]\n",
    "scores = [70,80,60]\n",
    "plt.bar(x,scores)\n",
    "plt.title('中文标题')\n",
    "plt.xlabel('班级')\n",
    "plt.ylabel('成绩')\n",
    "plt.xticks(x,name)\n",
    "\n",
    "#plt.text(1,71,80)\n",
    "for i in range(1,4):\n",
    "    plt.text(i,scores[i-1]+1,scores[i-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 饼图\n",
    "- 主题中各成分的比例情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x21d67a22848>,\n",
       "  <matplotlib.patches.Wedge at 0x21d67a2b6c8>,\n",
       "  <matplotlib.patches.Wedge at 0x21d67a327c8>,\n",
       "  <matplotlib.patches.Wedge at 0x21d67a3acc8>],\n",
       " [Text(-0.11498140519131439, 1.093974074857458, '房贷'),\n",
       "  Text(-0.8899185666875186, -0.6465639524941309, '饮食'),\n",
       "  Text(-0.11498104670295138, -1.0939741125360753, '出行'),\n",
       "  Text(0.8899188693658512, -0.6465635358931133, '教育')],\n",
       " [Text(-0.0627171301043533, 0.5967131317404315, '53.3%'),\n",
       "  Text(-0.485410127284101, -0.35267124681498047, '13.3%'),\n",
       "  Text(-0.0627169345652462, -0.5967131522924047, '13.3%'),\n",
       "  Text(0.48541029238137334, -0.35267101957806174, '20.0%')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lables = ['房贷','饮食','出行','教育']\n",
    "data = [8000,2000,2000,3000]\n",
    "plt.pie(data,labels=lables,autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 折线图\n",
    "- 主题的变化趋势"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d681ebbc8>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= range(2005,2020)\n",
    "y = [157,160,162,163,167,170,173,175,174,179,182,182,182,182,183]\n",
    "plt.title('中文标题')\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 阶梯图\n",
    "- 可以像折线图那样反映数据发展趋势，还可以反映数据状态的持续时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d69684a08>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= range(2005,2020)\n",
    "y = [157,160,162,163,167,170,173,175,174,179,182,182,182,182,183]\n",
    "plt.title('中文标题')\n",
    "plt.step(x,y)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 散点图\n",
    "- 隐变量随自变量而变化的大致趋势，据此可以选择合适的函数对数据点进行拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(20, 10, '洗衣液')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x= [18.9,21.3,19.5,20.5,19.9,22.3,21.4,9,10.4,9.3,11.6,11.8,\n",
    "    10.3,8.7,14.3,14.1,14,16.5,15.1,16.4,15.7]\n",
    "y =[10.4,8.7,11.6,9.7,9.4,11,10.6,9.4,9,11.3,8.5,10.4,10,9.5,\n",
    "    17.2,15.5,16.5,17.7,17.3,15,18]\n",
    "plt.scatter(x,y)\n",
    "plt.title('超市商品价格与销量散点图')\n",
    "plt.xlabel('价格')\n",
    "plt.ylabel('销量')\n",
    "plt.text(16,16,'牙膏')\n",
    "plt.text(10,12,'纸巾')\n",
    "plt.text(20,10,'洗衣液')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**拓展一下 最小二乘法拟合函数趋势**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1bf5bd3bbc8>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "\n",
    "#用最小二乘法获得截距\n",
    "z = np.polyfit(x,y,1)\n",
    "\n",
    "# 根据截距生成 多项式\n",
    "p = np.poly1d(z)\n",
    "\n",
    "#画出拟合曲线\n",
    "plt.plot(x,p(x),'r-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 箱线图\n",
    "- 反映原始数据分布的特征，还可以进行多组数据分布特征的比较"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![boxplot](https://gitee.com/Little_Six/repository_pic/raw/master/null/boxplot.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x21d67c91788>,\n",
       "  <matplotlib.lines.Line2D at 0x21d67c9ea48>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x21d67ca2908>,\n",
       "  <matplotlib.lines.Line2D at 0x21d67ca5fc8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x21d67c9e688>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x21d67ca5b48>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x21d67ca9688>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [77,70,72,89,89,70,90,87,94,63,81,99,94,80,95,67,65,88,60,67,30]\n",
    "plt.boxplot(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 极线图\n",
    "- 极坐标下的数据分布情况，多用于显示具有一定周期性的数据\n",
    "- projection = 'polar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21d69464348>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#极径与角度\n",
    "r = [1,2,3,4,5]#极径\n",
    "theta = [0,1.5707,3.14159,4.71238,6.283185]\n",
    "ax = plt.subplot(111,projection = 'polar')\n",
    "ax.plot(theta,r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 堆积图\n",
    "- 综合展示不同分类的指标趋势及他们的综合的趋势\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAD2CAYAAAAksGdNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAMVUlEQVR4nO3dYajd913H8ffHpJGYSE3pJVgfGAYFEdds9RobTPWiaVm7IqUOKtT5oJOAlD3xiS2tuInbgyHFUmwlWKUUVmw3KlUsTYXGxdnZ3myuqw9GYabTuNA7Uhsio2r5+uD+XZLbe5PTk3PP2f3e9wsO+Z/f/5zf+f7h3k9+9/f7//8nVYUkaWP7oVkXIEm6fIa5JDVgmEtSA4a5JDVgmEtSA1tn8aFXX3117dmzZxYfLUkb1vHjx79bVXOr7ZtJmO/Zs4fFxcVZfLQkbVhJ3lhrn9MsktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktTATK4AlaT3I5l1BZOzXt8H5MhckhowzCWpAcNckhowzCWpARdANxgXgiSt5pIj8yRXJnkuyZEkzyTZluTbSY4Ojw8Or/t0kleS/Mn6ly1JOt8o0yx3AQ9W1c3AKeBe4MmqWhge30jys8ABYB/wZpKD61eyJGmlS4Z5VT1SVS8MT+eA/wVuS/JykseSbAV+CfhiVRXwPHDjyn6SHEqymGRxaWlpgocgSRp5ATTJfmAX8AJwsKr2AVcAtwI7gJPDS08Du1e+v6oOV9V8Vc3Pza36FXaSpDGNtACa5CrgYeDXgFNV9c6waxG4FjgLbB/aduJZMpI0VaMsgG4Dngbuq6o3gCeS7E2yBbgd+DpwnOU5c4C9wIn1KVeStJpRRtCfAK4H7k9yFPgX4Angn4GXqurvgH8APpzkIYYF0vUpV5K0mtSETvZNsh34KPDVqvrWxV47Pz9fi4uLE/nczcbzzLUZ+XO/LMnxqppfbd/ELhqqqu8BX5hUf5Kk0blQKUkNGOaS1IBhLkkNGOaS1IBhLkkNGOaS1IBhLkkNGOaS1IBhLkkNGOaS1IBhLkkN+IXO0gbR5WZT3mBtfTgyl6QGDHNJasAwl6QGDHNJasAwl6QGDHNJasAwl6QGDHNJasAwl6QGDHNJasAwl6QGDHNJasAbbWnD6HKjKfBmU5q8DRnmXX6p/YWWNClOs0hSA4a5JDVgmEtSA4a5JDVwyTBPcmWS55IcSfJMkm1JHkvyUpIHznvde9okSdMxysj8LuDBqroZOAX8OrClqvYDH0hybZI7VratX8mSpJUueWpiVT1y3tM54DeAPx6eHwEOAB8GnlrR9vrkypQkXczIc+ZJ9gO7gH8DTg7Np4HdwI5V2la+/1CSxSSLS0tLl1W0JOlCI4V5kquAh4G7gbPA9mHXzqGP1douUFWHq2q+qubn5uYut25J0nlGWQDdBjwN3FdVbwDHWZ5GAdgLnFijTZI0JaNczv8J4Hrg/iT3A38BfDzJNcAtwA1AAcdWtEmSpmSUBdBHgUfPb0vyLHAT8LmqentoW1jZJkmajrFutFVVb3Hu7JU12yRJ0+EVoJLUgGEuSQ0Y5pLUgGEuSQ0Y5pLUgGEuSQ0Y5pLUgGEuSQ0Y5pLUwFhXgErSVH0qs65ggmpdenVkLkkNGOaS1IBhLkkNGOaS1IBhLkkNGOaS1ICnJm40nqIlaRWOzCWpAcNckhowzCWpAcNckhowzCWpAcNckhowzCWpAcNckhowzCWpAcNckhowzCWpAcNckhowzCWpAcNckhoYKcyT7E5ybNj+iST/nuTo8Jgb2h9L8lKSB9azYEnSe13yfuZJdgGPAzuGpp8HPlNVj573mjuALVW1P8mfJ7m2ql5fl4qlzarNvey9j/16GGVk/i5wJ3BmeH4D8FtJvprks0PbAvDUsH0EOLCykySHkiwmWVxaWrq8qiVJF7hkmFfVmap6+7ym51gO758D9ie5juVR+8lh/2lg9yr9HK6q+aqan5ubu+zCJUnnjPO1cf9YVe8AJPkacC1wFtg+7N+JC6uSNFXjhO7zSX48yY8ANwOvAcc5N7WyFzgxmfIkSaMYZ2T+aeBF4L+BP62qbyb5DnAsyTXALSzPq0uSpmTkMK+qheHfF4GfWrHvTJIF4Cbgcyvm2CVJ62yckfmqquotzp3RIkmaIhcqJakBw1ySGjDMJamBic2ZS+uuzeXs4CXtmjRH5pLUgGEuSQ1szGmWNn9u+6e2pMlwZC5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktSAYS5JDRjmktTASGGeZHeSY8P2FUn+OsmXk9y9VpskaXouGeZJdgGPAzuGpk8Cx6vqF4CPJfnRNdokSVMyysj8XeBO4MzwfAF4atj+EjC/RtsFkhxKsphkcWlp6TJKliStdMkwr6ozVfX2eU07gJPD9mlg9xptK/s5XFXzVTU/Nzd3eVVLki4wzgLoWWD7sL1z6GO1NknSlIwTuseBA8P2XuDEGm2SpCnZOsZ7Hgf+NsmNwE8D/8TyFMvKNknSlIw8Mq+qheHfN4CbgC8DB6vq3dXa1qFWSdIaxhmZU1X/wbmzV9ZskyRNx1hhLknTVJ+adQUT9Pvr061nnUhSA4a5JDVgmEtSA4a5JDVgmEtSA4a5JDVgmEtSA4a5JDXgRUMbjBdPSFqNI3NJasCRubRBtPmrzL/I1oUjc0lqwDCXpAYMc0lqYEPOmTt3KEkXcmQuSQ0Y5pLUgGEuSQ0Y5pLUwIZcANXm1GbhG1z81sQ5MpekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrgfV/On2Qr8K3hAfBJ4GPArcDLVXXP5MqTJI1inJH5dcCTVbVQVQvANuAAsA94M8nBCdYnSRrBOGF+A3BbkpeTPAb8CvDFqirgeeDG1d6U5FCSxSSLS0tL41csSXqPccL8FeBgVe0DrgC2AyeHfaeB3au9qaoOV9V8Vc3Pzc2NVawkaXXj3AL31ap6Z9he5FygA+zERVVJmrpxgveJJHuTbAFuB3awPGcOsBc4MaHaJEkjGmdk/gfA54EAzwJ/CBxL8hDwkeEhSZqi9x3mVfUay2e0fN9wBstHgYeq6l8nVJskaUQT+dq4qvoe8IVJ9CVJev9crJSkBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBgxzSWrAMJekBiYa5kkeS/JSkgcm2a8k6eImFuZJ7gC2VNV+4ANJrp1U35Kki9s6wb4WgKeG7SPAAeD1/9+Z5BBwaHh6Nsk3J/jZ6+Fq4Lvr+gnJunZ/Gdb/2GFzH7/H/oPqB/34f3KtHZMM8x3AyWH7NHD9+Tur6jBweIKft66SLFbV/KzrmIXNfOywuY9/Mx87bOzjn+Sc+Vlg+7C9c8J9S5IuYpKBe5zlqRWAvcCJCfYtSbqISU6z/BVwLMk1wC3ADRPsexY2zJTQOtjMxw6b+/g387HDBj7+VNXkOkt2ATcBX6qqUxPrWJJ0URMNc0nSbLhIKZ0nyVVJbkpy9axrkd4Pw3wVSXYnOTbrOqYtyZVJnktyJMkzSbbNuqZpGqYJ/wbYB7yYZG7GJU3d8LP/tVnXMU1Jtib5dpKjw+ODs65pHIb5CsMv9OMsnze/2dwFPFhVNwOngI/MuJ5puw74nar6DPA8K66V2CT+iHOnGG8W1wFPVtXC8PjGrAsah2H+Xu8CdwJnZl3ItFXVI1X1wvB0DnhzlvVMW1X9fVV9Jckvsjw6f2nWNU1Tkl8G/ovl/8g3kxuA25K8PNxfapJn+U2NYb5CVZ2pqrdnXccsJdkP7Kqqr8y6lmlLEpb/M38L+J8ZlzM1w5Ta7wH3zrqWGXgFOFhV+4ArgFtnXM9YDHNdIMlVwMPA3bOuZRZq2T3Aq8CvzrqeKboXeKSq/nPWhczAq1X1nWF7EdiQNwk0zPV9w+jsaeC+qnpj1vVMW5LfTfKbw9MfAzZTsB0E7klyFPhQkj+bcT3T9ESSvUm2ALcDX591QePwPPM1JDlaVQuzrmOakvw28FnO/TA/WlV/OcOSpmpY/H4K+GHgNeCe2oS/IJvtZz/JzwCfBwI8W1X3z7iksRjmktSA0yyS1IBhLkkNGOaS1IBhLkkNGOaS1IBhLkkN/B9zkrzU5p8hCQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ch = [72,80,66,77,92]\n",
    "math = [62,92,72,75,88]\n",
    "eng = [76,81,73,75,80]\n",
    "\n",
    "plt.bar(range(1,6),ch,color = 'r',label = 'chinese')\n",
    "plt.bar(range(1,6),math,bottom=ch,color = 'g',label = 'math')\n",
    "chmath = [ch[i]+math[i] for i in range(5)]\n",
    "plt.bar(range(1,6),eng,bottom=chmath,color = 'b',label = 'english')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分块图\n",
    "- 将不同数据集进行并列显示，优势在于可以直观反映数据集之间的差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '成绩')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "name_list = ['语文','数学','英语']\n",
    "c1 = [81.4,83,87.1]\n",
    "c2 = [85.6,87.4,90]\n",
    "c3 = [78,81.2,86.1]\n",
    "\n",
    "width = 0.4\n",
    "x= [1,3,5]\n",
    "\n",
    "plt.bar(x,c1,label = 'class1',fc = 'r',width = width)\n",
    "\n",
    "x= [1.4,3.4,5.4]\n",
    "plt.bar(x,c2,label = 'class2',fc = 'g',width = width)\n",
    "#plt.bar(x+width,y)\n",
    "\n",
    "x= [1.8,3.8,5.8]\n",
    "plt.bar(x,c3,label = 'class3',fc = 'b',width = width)\n",
    "\n",
    "x= [1.4,3.4,5.4]\n",
    "plt.xticks(x,name_list)\n",
    "\n",
    "plt.legend()\n",
    "plt.title('三班成绩')\n",
    "plt.xlabel('科目')\n",
    "plt.ylabel('成绩')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 气泡图\n",
    "- 展示三个变量之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x21d6e09e2c8>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(2005,2020)\n",
    "y = [157,160,162,163,167,170,173,175,174,179,182,182,182,182,183]\n",
    "z = [70,220,50,210,60,270,50,260,40,230,30,240,50,250,30]\n",
    "plt.scatter(x,y,s = z )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
